Ranking
How to log your model schema for ranking models
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How to log your model schema for ranking models
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There are four key ranking model use cases to consider:
Search Ranking
Collaborative Filtering Recommender Systems
Content Filtering Recommender Systems
Classification-Based Ranking Models
Different metrics are used for ranking model evaluation based on your model use case, score, and label availability. The case determines the available performance metrics. Click for all valid model types and metric combinations.
Model predicts score used to rank
rank, relevance score
Model predicts binary actions a user can take which is used to rank
rank, relevance_labels
Model predicts multiple actions a user can take which is used to rank
rank, relevance_labels (list of strings)
Model can also be evaluated using AUC + LogLoss
Ranking Case 2 or 3, prediction score
Ranking models have a few unique model schema fields that help Arize effectively monitor, trace, and visualize your ranking model data.
Prediction Group ID: A subgroup of prediction data. Max 100 ranked items within each group
Rank: Unique value within each prediction group (1-100)
Relevancy Score/Label: Ground truth score/label associated with the model
In the ranking model context, a relevance score is a ground truth numerical score where the higher the relevance_score, the more important the item is. For example, if an item was clicked on it may have a relevance score of 0.5, whereas if it was purchased that relevancy score would be 1.
rank and relevance_score are required to compute rank-aware evaluation metrics on your model.
rank
int from 1-100
1
relevance_score
numeric (float | int)
0.5
prediction_group_id
string limited to 128 characters
148
rank
int from 1-100
1
relevance_labels
string
“click”
prediction_group_id
string limited to 128 characters
148
Since ground truth can contain multiple events, you can pass in multiple ground truth labels in a list.
rank
int from 1-100
1
relevance_labels
list of strings
[“click”, “favorite”, “buy”]
prediction_group_id
string limited to 128 characters
148
AUC and LogLoss are computed based on prediction_score and relevance_labels (or default relevance_labels in the case of multi-label).
rank
int from 1-100
1
prediction_score
float
0.5
prediction_group_id
string limited to 128 characters
148
Rank-aware evaluation metrics: NDCG @k (MAP @K & MRR coming soon)
Evaluation metrics: AUC, PR-AUC, LogLoss
A relevance score is required to calculate rank-aware evaluation metrics. If your relevance_score is unavailable, the Arize platform will calculate a relevance_score using a simple attribution model with a prediction label and a relevance label. Arize computes a binary relevance value (0/1) based on the default positive class.
Positive class "buy" and relevance label is "buy" --> relevance will be attributed to 1.
Positive class "buy" and relevance label is else --> relevance will be attributed to 0.
Positive class "buy" and relevance labels are ["buy", "click", "scroll"] --> relevance will be attributed to sum([1,0,0])
Ranking model: Assigns a rank to each item in a prediction group (also known as a batch or query), across many possible groups.
Prediction Group: A group of predictions within which items are ranked.
Example: A user of a hotel booking site types in a search term (“skiing”) and is presented with a list of results representing a single query
Rank: The predicted rank of an item in a prediction group (Integer between 1-100).
Example: Each item in the search prediction group has a rank determined by the model (i.e. Aspen is assigned rank=1, Tahoe is assigned rank=2, etc. based on input features and query features to the model)
Relevance Score (i.e. Actual Scores): The ground truth relevance score (numeric). Higher scores denote higher relevance.
Example: Each item in the search prediction group has a score determined by the action a user took on the item (i.e. “clicking” on an item indicates relevance score = 0.5, purchasing an item indicates relevance score = 1)
Rank-Aware Evaluation Metric: A rank-aware evaluation metric is an evaluation metric that gauges rank order and relevancy of predictions.
Rank-aware evaluation metrics include NDCG, MRR, and MAP. Note that MRR and MAP also require relevance_labels to be provided to be computed.
, , ,
, , ,
, , , , AUC, PR-AUC,
Download an example Parquet file: Open parquet reader.
In this case, relevance_scoredoes not need to be passed in. Since relevance_score is required to compute rank-aware evaluation metrics, Arize uses an attribution model to create a relevance_score based on your positive class and relevance_labels. Learn more about our attribution model .
Download an example Parquet file: Open parquet reader.
In this case relevance_score does not need to be passed in. Since relevance_score is required to compute rank-aware evaluation metrics, Arize uses an attribution model to create a relevance_score based on your positive class and relevance_labels. Learn more about our attribution model .
Download an example Parquet file: Open parquet reader.
Normalized discounted cumulative gain (NDCG) is a that measures a model's ability to rank query results in the order of the highest relevance (graded relevance). You can read more about how NDCG is computed .
Arize supports ,, and ranking models
